Methods, systems, and devices for identifying an IP address from a mixed data pool using artificial intelligence

ABSTRACT

Aspects of the subject disclosure may include, for example, embodiments that include obtaining a first data pool of different types of data. The different types of data include a first group of IPv4 addresses. Further embodiments include selecting a first portion of the first group of IPv4 addresses using artificial intelligence techniques and storing the first portion of the first group of IPv4 addresses in a first table of IPv4 addresses. Additional embodiments can include assigning a first IPv4 address in the first table to a first computing device in response to determining the first IPv4 address in the first table is not currently being used. Other embodiments are disclosed.

FIELD OF THE DISCLOSURE

The subject disclosure relates to methods, systems, and devices foridentifying an IP address from a mixed data pool using artificialintelligence.

BACKGROUND

Automated systems for provisioning an IP address to a computing devicecan result in provisioning an IP address to two different computingdevices (e.g., duplicate IP address assigned to two different computingdevices). Consequently, a computing device with a duplicate IP addresscan lose service for a period of time, while a patch can be developedand delivered to at least one of the computing devices to provision itwith a different IP address, thereby re-instating service to thecomputing device.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made to the accompanying drawings, which are notnecessarily drawn to scale, and wherein:

FIG. 1 is a block diagram illustrating an exemplary, non-limitingembodiment of a communications network in accordance with variousaspects described herein.

FIGS. 2A-B are block diagrams illustrating example, non-limitingembodiments of systems functioning within the communication network ofFIG. 1 in accordance with various aspects described herein.

FIGS. 2C-D depicts illustrative embodiments of methods in accordancewith various aspects described herein.

FIG. 2E is a block diagram illustrating an example, non-limitingembodiment of systems functioning within the communication network ofFIG. 1 in accordance with various aspects described herein.

FIGS. 2F-G depicts illustrative embodiments of methods in accordancewith various aspects described herein.

FIG. 2H is a block diagram illustrating an example, non-limitingembodiment of systems functioning within the communication network ofFIG. 1 in accordance with various aspects described herein.

FIG. 3 is a block diagram illustrating an example, non-limitingembodiment of a virtualized communication network in accordance withvarious aspects described herein.

FIG. 4 is a block diagram of an example, non-limiting embodiment of acomputing environment in accordance with various aspects describedherein.

FIG. 5 is a block diagram of an example, non-limiting embodiment of amobile network platform in accordance with various aspects describedherein.

FIG. 6 is a block diagram of an example, non-limiting embodiment of acommunication device in accordance with various aspects describedherein.

DETAILED DESCRIPTION

The subject disclosure describes, among other things, illustrativeembodiments for obtaining a first data pool of different types of data.The different types of data include a first group of IPv4 addresses.Further embodiments can include selecting a first portion of the firstgroup of IPv4 addresses using artificial intelligence techniques, andstoring the first portion of the first group of IPv4 addresses in afirst table of IPv4 addresses. Additional embodiments can includeassigning a first IPv4 address in the first table to a first computingdevice in response to determining the first IPv4 address in the firsttable is not currently being used. Other embodiments are described inthe subject disclosure.

One or more aspects of the subject disclosure include a device,comprising a processing system including a processor, and a memory thatstores executable instructions that, when executed by the processingsystem, facilitate performance of operations. The operations can includeobtaining a first data pool of different types of data. The differenttypes of data include a first group of IPv4 addresses. Furtheroperations can include selecting a first portion of the first group ofIPv4 addresses using artificial intelligence techniques, and storing thefirst portion of the first group of IPv4 addresses in a first table ofIPv4 addresses. Additional operations can include assigning a first IPv4address in the first table to a first computing device in response todetermining the first IPv4 address in the first table is not currentlybeing used.

One or more aspects of the subject disclosure include a machine-readablemedium, comprising executable instructions that, when executed by aprocessing system including a processor, facilitate performance ofoperations. The operations can include obtaining a first data pool ofdifferent types of data. The different types of data include a firstgroup of IPv4 addresses. Further operations can include selecting afirst portion of the first group of IPv4 addresses using artificialintelligence techniques, and storing the first portion of the firstgroup of IPv4 addresses in a first table of IPv4 addresses. Additionaloperations can include assigning a first IPv4 address in the first tableto a first computing device in response to determining the first IPv4address in the first table is not currently being used. Also, operationscan include removing a second IPv4 address from the first table inresponse to determining the second IPv4 address in the first table iscurrently being used by a second computing device.

One or more aspects of the subject disclosure include a method. Themethod can include training, by a processing system including aprocessor, artificial intelligence techniques on training data torecognize IPv4 addresses from a first data pool that includes differenttypes of data. Further, the method can include obtaining, by theprocessing system, a second data pool of the different types of data.The different types of data include a first group of IPv4 addresses. Inaddition, the method can include selecting, by the processing system, afirst portion of the first group of IPv4 addresses using artificialintelligence techniques, and storing, by the processing system, thefirst portion of the first group of IPv4 addresses in a first table ofIPv4 addresses. Also, the method can include assigning, by theprocessing system, a first IPv4 address in the first table to a firstcomputing device in response to determining the first IPv4 address inthe first table is not currently being used.

Referring now to FIG. 1, a block diagram is shown illustrating anexample, non-limiting embodiment of a communications network 100 inaccordance with various aspects described herein. For example,communications network 100 can facilitate in whole or in partidentifying an IP address from a pool of different types of data thatinclude a group of IP addresses and determining whether the IP addressis currently being used by a computing device. In particular, acommunications network 125 is presented for providing broadband access110 to a plurality of data terminals 114 via access terminal 112,wireless access 120 to a plurality of mobile devices 124 and vehicle 126via base station or access point 122, voice access 130 to a plurality oftelephony devices 134, via switching device 132 and/or media access 140to a plurality of audio/video display devices 144 via media terminal142. In addition, communication network 125 is coupled to one or morecontent sources 175 of audio, video, graphics, text and/or other media.While broadband access 110, wireless access 120, voice access 130 andmedia access 140 are shown separately, one or more of these forms ofaccess can be combined to provide multiple access services to a singleclient device (e.g., mobile devices 124 can receive media content viamedia terminal 142, data terminal 114 can be provided voice access viaswitching device 132, and so on).

The communications network 125 includes a plurality of network elements(NE) 150, 152, 154, 156, etc. for facilitating the broadband access 110,wireless access 120, voice access 130, media access 140 and/or thedistribution of content from content sources 175. The communicationsnetwork 125 can include a circuit switched or packet switched network, avoice over Internet protocol (VoIP) network, Internet protocol (IP)network, a cable network, a passive or active optical network, a 4G, 5G,or higher generation wireless access network, WIMAX network,UltraWideband network, personal area network or other wireless accessnetwork, a broadcast satellite network and/or other communicationsnetwork.

In various embodiments, the access terminal 112 can include a digitalsubscriber line access multiplexer (DSLAM), cable modem terminationsystem (CMTS), optical line terminal (OLT) and/or other access terminal.The data terminals 114 can include personal computers, laptop computers,netbook computers, tablets or other computing devices along with digitalsubscriber line (DSL) modems, data over coax service interfacespecification (DOCSIS) modems or other cable modems, a wireless modemsuch as a 4G, 5G, or higher generation modem, an optical modem and/orother access devices.

In various embodiments, the base station or access point 122 can includea 4G, 5G, or higher generation base station, an access point thatoperates via an 802.11 standard such as 802.11n, 802.11ac or otherwireless access terminal. The mobile devices 124 can include mobilephones, e-readers, tablets, phablets, wireless modems, and/or othermobile computing devices.

In various embodiments, the switching device 132 can include a privatebranch exchange or central office switch, a media services gateway, VoIPgateway or other gateway device and/or other switching device. Thetelephony devices 134 can include traditional telephones (with orwithout a terminal adapter), VoIP telephones and/or other telephonydevices.

In various embodiments, the media terminal 142 can include a cablehead-end or other TV head-end, a satellite receiver, gateway or othermedia terminal 142. The display devices 144 can include televisions withor without a set top box, personal computers and/or other displaydevices.

In various embodiments, the content sources 175 include broadcasttelevision and radio sources, video on demand platforms and streamingvideo and audio services platforms, one or more content data networks,data servers, web servers and other content servers, and/or othersources of media.

In various embodiments, the communications network 125 can includewired, optical and/or wireless links and the network elements 150, 152,154, 156, etc. can include service switching points, signal transferpoints, service control points, network gateways, media distributionhubs, servers, firewalls, routers, edge devices, switches and othernetwork nodes for routing and controlling communications traffic overwired, optical and wireless links as part of the Internet and otherpublic networks as well as one or more private networks, for managingsubscriber access, for billing and network management and for supportingother network functions.

FIGS. 2A-B and 2H are block diagrams illustrating example, non-limitingembodiments of systems functioning within the communication network ofFIG. 1 in accordance with various aspects described herein. Referring toFIG. 2A, in one or more embodiments, a system 200 can be used toautomatically and dynamically provision an IP address to one or morecomputing devices 212, 214. Further, system 200 can include a server 202communicatively coupled to a group of computing devices 212, 214 over acommunication network 207. In addition, the system 200 can include theserver 202 communicatively coupled to one or more databases 204, 206over a communication network 203. In some embodiments, one database 204can include training data and another database 206 can include a datapool of different types of data that can include IP addresses The IPaddresses can include IPv4 addresses or IPv6 addresses as well as otheraddress mechanisms that can be used to identify computing devices. Inother embodiments, the training data and data pool can be stored in onedatabase. Also, the server 202 can be communicatively coupled to aninformation repository 209 (e.g. database, server, etc.) over acommunication network 205. The information repository 209 can include atable 208 of IPv4 addresses and a table 210 IPv6 addresses. Thecommunication networks 203, 205, 207 can include a wired communicationnetwork, wireless communication network, or a combination thereof. Thecomputing devices 212 can include, but not limited to, a desktopcomputer, laptop computer, tablet computer, mobile device, mobile phone,wearable device, smartwatch, or any other computing device.

In one or more embodiments, the server 202, operated by an entity, canstore and execute artificial intelligence techniques that can be asoftware application to recognize an IP address from a data pool storedon a database 206, and determine whether the IP address is currentlybeing used by a computing device within the entity, and if not,provision the IP address to a computing device operated by the entity.The entity can be a company, non-profit, or any organization thatoperates computing devices that use one or more IP addresses. The IPaddress can be an IPv4 address, an IPv6 address, or any other type of IPaddress or addressing mechanism for computing devices. In furtherembodiments, prior to attempting to recognize an IP address from thedata pool, the artificial intelligence techniques can be trained toidentify an IP address from training data stored in a database 204. Inadditional embodiments, the artificial intelligence techniques can beimplemented by a neural network. Embodiments that are directed totraining the artificial intelligence techniques implemented by theneural network word are discussed when describing FIGS. 2E-2G. In someembodiments, the database 204 and database 206 can be the same database.In other embodiments, the database 204 and database 206 can be separatedatabases. In further embodiments, the database 204, database 206, andserver 202 can be one computing device.

In one or more embodiments, the server 202 trains the artificialintelligence techniques on training data to recognize an IP address (orother addressing mechanism). In some embodiments, the server 202obtains, over communication network 203, the training data from database204. Further, the training data can include a data pool that includesthe different types of data or a mix of data. The different types ofdata can include IPv4 addresses, IPv6 addresses as well as other data(e.g., such as personal identifiable information that can include aperson's name, physical address, telephone number, email address,location coordinates, location data, or any other data, etc.).

In one or more embodiments, the server 202 can obtain, over thecommunication network 203, a data pool of different types of data fromdatabase 206 that may be similar to the different types of data in thetraining data. The different types of data can include IPv4 addresses,IPv6 addresses as well as other data (e.g., such as personalidentifiable information that can include a person's name, physicaladdress, telephone number, email address, location coordinates, locationdata, or any other data, etc.).

The different types of data can include a group of IP addresses.Further, the server can select, recognize, or identify, a portion of thegroup of IP addresses from the data pool using the artificialintelligence techniques. In addition, the server 202 can store theportion of the group of IP addresses in a table 208, 210 of IPaddresses. The table 208, 210 of IP addresses can be in an informationrepository 209 that can be a database, web server, or any other type ofinformation repository. Further, the server 202 can send the portion ofthe group of IP addresses to the information repository 209 over acommunication network 205. Further, the server 202 can select,recognize, or identify IPv4 addresses or IPv6 addresses from the portionof the group of IP addresses. In some embodiments, the server can storeIPv4 for addresses in one table 208 and IPv6 addresses in another table210. In other embodiments, the information repository 209 can be part ofserver 202.

In one or more embodiments, the server 202 can recognize IP address fromthe data pool 206 in advance. Further, the identified IP address can bestored in tables 208, 210. Further, the server 202 can have twofunctions. Prior to run time, server 202 has artificial intelligence torecognize an IP address. During run time, when a service request isreceived, the server 202 can utilize a search engine to quickly searchtables 208, 210 determine whether an identified IP address matches an IPaddress in tables 208, 210. (See FIG. 2H and its description herein).

In one or more embodiments, the server 202 can access an IP address froma table 208, 210 (over a communication network 205) and determine the IPaddress is not currently being used by a computing device operated bythe entity. For example, the server 202 can compare the IP addressaccessed form the table 208, 210 with a list of IP addresses currentlybeing used by computing devices within the entity to determine whetherthe IP address accessed from the table 210, 212 is currently in use(such a list of currently used IP addresses can be maintained byperiodically querying/obtaining IP addresses of the entity's computingdevices and storing them in the list). If not, the server 202 can assignthe IP address to a computing device 212, over a communication network207.

In one or more embodiments, the server 202 can access an IP address froma table 208, 210 (over a communication network 205) and determine the IPaddress is currently used by a computing device 214 operated by theentity, as described herein. Thus, the server 202 can remove the IPaddress from the table so that it will not mistakenly be assigned to acomputing device in the future, thereby creating IP address conflict byhaving two computing devices assigned the same IP address.

In one or more embodiments, the server 202 can include the selection,recognition, or identification of the portion of IP addresses from thegroup of IP addresses from the data pool to the training data. That is,the server 202 can send the selection, recognition, or identification ofthe IP addresses to the database 204, over the communication network203, to be stored in database 204 and included in the training data tobe used in the future.

In one or more embodiments, the server 202 can recognize several IPv4addresses and several IPv6 addresses from the data pool stored indatabase 206 and determine whether they are currently being used bycomputing devices operated by the entity. The server 202 can obtain thetraining data from the database 204, over communication network 203, andtrain the artificial intelligence techniques on training data torecognize IPv4 addresses from training data stored in database 204. Thetraining data can be a data pool that includes the different types ofdata. Further, the server 202 can obtain a data pool of different typesof data from database 206 over communication network 203. The differenttypes of data include a group of IPv4 addresses. In addition, the server202 can select a portion of the group of IPv4 addresses using artificialintelligence techniques. In addition, the server 202 can store theportion of the group of IPv4 addresses in a table 208 of IPv4 addressesin information repository 209, in which the portion of the IPv4addresses are sent to the information repository 209 over communicationnetwork 205 to be stored in table 208. Also, the server 202 can accessan IPv4 address from the table 208 in information repository 209 overcommunication network 205 and assign the first IPv4 address to acomputing device 212 over communication network 207 in response to theserver 202 determining the IPv4 address is not currently being used (asdescribed herein).

In one or more embodiments, the server 202 can access an IPv4 addressfrom the table 208 in information repository 209 over communicationnetwork 205 and provide instructions to the information repository 209to remove the IPv4 address from the table 208 in response to the server202 determining the IPv4 address is currently being used by a computingdevice 214 (as described herein).

In one or more embodiments, the server 202 can include the selecting ofthe portion of the group of IPv4 address for training by sending theselection of the portion of the IPv4 addresses to the database 204 to beincluded into the training data to be used in the future.

In one or more embodiments, the server 202 can obtain the training datafrom the database 204, over communication network 203, and train theartificial intelligence techniques on training data to recognize IPv6addresses from training data stored in database 204. The training datacan be a data pool that includes the different types of data. Further,the server 202 can obtain a data pool of different types of data fromdatabase 206 over communication network 203. The different types of datainclude a group of IPv6 addresses. In addition, the server 202 canselect a portion of the group of IPv6 addresses using artificialintelligence techniques. In addition, the server 202 can store theportion of the group of IPv6 addresses in a table 210 of IPv6 addressesin information repository 209, in which the portion of the IPv6addresses are sent to the information repository 209 over communicationnetwork 205 to be stored in table 210. Also, the server 202 can accessan IPv6 address from the table 210 in information repository 209 overcommunication network 205 and assign the first IPv6 address to acomputing device 212 over communication network 207 in response to theserver 202 determining the IPv6 address is not currently being used (asdescribed herein).

In one or more embodiments, the server 202 can access an IPv6 addressfrom the table 210 in information repository 209 over communicationnetwork 205 and provide instructions to the information repository 209to remove the IPv6 address from the table 210 in response to the server202 determining the IPv6 address is currently being used by a computingdevice 214 (as described herein).

In one or more embodiments, the server 202 can include the selecting ofthe portion of the group of IPv6 address for training by sending theselection of the portion of the IPv6 addresses to the database 204 to beincluded into the training data to be used in the future.

Referring to FIG. 2B, in one or more embodiments, a system 220 cancomprise the server 202 that includes artificial intelligence techniquesimplemented by a neural network 222. Further, the neural network 222 caninclude an input layer 224, several hidden layers 226, 228, 230, and anoutput layer 232. Although neural network 222 is shown with three hiddenlayers 226, 228, 230, the neural network 222 can include any number ofhidden layers.

In one or more embodiments, the neural network 222 can be trained usingtraining data to recognize IP address from a mixed data pool (e.g.,different types of data) (details of the training of the artificialintelligence techniques are discussed when describing FIGS. 2E-2G). Infurther embodiments, a data pool can be provided to the input layer 224and the data pool can be processed by the neural network 222. The datapool can include different types of data such as IP addresses and Non-IPaddresses, as described herein. The neural network 222 can select,recognize, or identify an IP address 234 from the data pool and datathat is a Non-IP address 236. The IP address 234 and/or the Non-IPaddress 236 can provided by the output layer 232 of the neural network222.

Referring to FIG. 2H, in one or more embodiments, system 200 b caninclude a requesting platform 202 b, address manager 204 b, a data pool206 b, a search engine 208 b, and a database 210 b. A requestingplatform 202 b can be a server or client computing device (or a group ofsuch servers or computing devices or an entity representing a group ofservers/computing devices) that is requesting a provisioning of an IPaddress for one of its computing devices. The address manager 204 baccesses data from a data pool 206 b. The data pool 206 b can be storedin a database that can be similar to the data pool 206 described in FIG.2A. The address manager 204 b can identify an IP address from the datafrom data pool 206 b using a neural network as described herein.Database 210 b can include information repository 209 that comprisestables 208, 210 in FIG. 2A. A search engine 208 b can search database210 b for the IP address identified by the address manager 204 b todetermine whether it is already in use, and if not, notify the addressmanager 204 b that it can provision the IP address.

In one or more embodiments, the system 200 b can implements a series ofsteps to provide an IP address to the requesting platform 202 b. At step212 b, the requesting platform 202 b can request an IP address from theaddress manager 204 b. At step 214 b, the address manager 204 b canidentify an IP address from the data pool 206 b and remove it from thedata pool 206 b. At step 216 b, the address manager 204 b provides theidentified IP address to the search engine 208 b. At step 218 b, thesearch engine 208 b searches the tables 208, 210 stored in database 210b to determine whether the identified IP address is already stored inthe tables 208, 210 (indicating that it may already be in use). At step,220 b, if no match is found, the system continues to step 222 b, inwhich the search engine 208 b notifies the address manager 204 b thatnot match has been found. If a match is found, then a request is sent tothe address manager 204 b to identify another IP address. At step 222 b,the IP address is provided to the requesting platform 202 b to provisiona computing device by the address manager 204 b. The functions of theaddress manager and search engine can be implemented by a server 202 inFIG. 2A or across multiple servers. In some embodiments, the functionsof the address manager can be in one server and the functions of thesearch engine can be in another server. Such servers can be managed byone entity or managed by several entities.

FIGS. 2C-D depicts illustrative embodiments of methods in accordancewith various aspects described herein. The methods in FIGS. 2C-D can beimplemented by a server, servers, or other computing device(s).Referring to FIG. 2C, in one or more embodiments, the method 240 caninclude the server, at 242, training artificial intelligence techniqueson training data to (automatically, with no or little human interventionas discussed in describing FIGS. 2E-G) recognize IP addresses from adata pool that includes different types of data. Further, the method 240can include the server, at 244, obtaining a first data pool of differenttypes of data. The different types of data include a first group of IPaddresses as well as include personal identifiable information that caninclude a person's name, physical address, telephone number, emailaddress, location coordinates, location data, or any other data. Inaddition, the method 240 can include the server, at 246, selecting afirst portion of the first group of IP addresses using the artificialintelligence techniques. Also, the method 240 can include the server, at248, storing the first portion of the first group of IP addresses in afirst table of IP addresses.

In one or more embodiments, the method 240 can include the server, at250, accessing a first IP address from the first table and determiningwhether the first IP address in the first table is not currently beingused (as described herein). If so, the method 240 can include theserver, at 252, assigning a first IP address in the first table to afirst computing device. If not, the method 240 can include the server,at 254, removing the first IP address from the first table. Further, themethod 240 can include the server, at 256, including the selection ofthe first portion of the first group of IP addresses into the trainingdata for future training purposes. The IP address can be an IPv4 addressor an IPv6 address.

Referring to FIG. 2D, in one or more embodiments, the method 260 caninclude the server, at 262, training artificial intelligence techniqueson training data to recognize IP addresses from a data pool thatincludes different types of data (details of the training of theartificial intelligence techniques are discussed when describing FIGS.2E-2G). Further, the method 260 can include the server, at 264,obtaining a first data pool of different types of data. The differenttypes of data include a first group of IP addresses as well as includepersonal identifiable information that can include a person's name,physical address, telephone number, email address, location coordinates,location data, or any other data. In addition, the method 260 caninclude the server, at 266, selecting a first portion of the first groupof IP addresses using the artificial intelligence techniques. Also, themethod 260 can include the server, at 268, storing the first portion ofthe first group of IP addresses in a first table of IP addresses.

In one or more embodiments, the method 260 can include the server, at270, accessing a first IP address from the first table and determiningthe first IP address in the first table is not currently being used (asdescribed herein). Further, the method 260 can include the server, at272, assigning a first IP address in the first table to a firstcomputing device. In addition, the method 260 can include the server, at274, accessing a second IP address from the first table and determiningthe second IP address in the first table is currently being used byanother computing device (as described herein). Also, the method 260 caninclude the server 276, removing the first IP address from the firsttable. Further, the method 260 can include the server, at 278, includingthe selection of the first portion of the first group of IP addressesinto the training data for future training purposes. The IP address canbe an IPv4 address or an IPv6 address.

FIG. 2E is a block diagram illustrating example, non-limiting embodimentof systems functioning within the communication network of FIG. 1 inaccordance with various aspects described herein. In one or moreembodiments, the system 200 a can be used for training a neural network206 a. In addition, the system 200 a includes training data 202 a,neural network 206 a, comparison dataset 208 a, server 210 a, andcomputing device 212 a operated by a user 214 a. Training data 202 a canbe stored in a database 204 as shown in FIG. 2A. The training data 202 acan list an individual's (e.g., employee) personal information such asname, address, different telephone numbers, label for employee computer,and IP address of employee computer. In other embodiments, training datacan comprise personal identifiable information that can include aperson's name, physical address, telephone number, email address,location coordinates, location data, or any other data. Further, theneural network 206 a can be similar or same neural network described inFIG. 2B. The server 210 a can store the comparison dataset 208 a, and/orimplement the neural network 206 a. Further, the server 210 a can beaccessed by a user 214 a by a computing device 212 a. The comparisondataset 208 a can be used by the server 210 a to determine whether theneural network 206 a correctly identifies an IP address or Non-IPaddress data for training of the neural network 206 a. That is, theserver 210 a compares the data structure of the comparison dataset 208 awith the data structure of the data identified as an IP address by theneural network 206 a and/or identified as Non-IP address data by theneural network 206 a. The computing device 212 a can include a laptopcomputer, tablet computer, mobile phone, desktop computer, or any othercomputing device. The user 214 a can be an employee or other personnelof the employer company that administers IP addresses for the employercompany.

In one or more embodiments, the training data 202 a can be provided tothe neural network 206 a as input for training purposes. The neuralnetwork 206 a can be implemented by a server 210 a. The neural network206 a can be trained to recognize data listed in the training data 202 aas either an IP address or as Non-IP address data. In some embodiments,a first output of the neural network 206 a can be a data item 203 a fromthe training data 202 a that the neural network 206 a recognizes oridentifies as an IP address and a second output of the neural network206 a can be a data item 205 a from the training data 202 a that theneural network 206 a recognizes or identifies as Non-IP address data.Further, the neural network 206 a can determine the likelihood that theneural network 206 a correctly identifies the data item 203 a as an IPaddress as well as determine the likelihood 213 a that the neuralnetwork 206 a correctly identifies a data item 205 a as Non-IP addressdata. In addition, the server 210 a can compare the data item 203 a to acomparison dataset 208 a to determine whether the data structure of dataitem 203 a conforms to the data structure of an IP address as show inthe comparison dataset 208 a. The data structure of an IP address can bea series four three-digit numbers ranging from 0 and 255 separated by aperiod. The comparison dataset 208 a can indicate this data structure.In other embodiments, the comparison dataset 208 a can indicate the datastructure of an IPv6 address or the data structure of some otheraddressing mechanism.

In some embodiments, the server 210 a compares the data structure of thedata item 203 a and determines whether the data structure of the dataitem 203 a conforms to the comparison dataset 208 a. If so, the server210 a uses the example training scenario as training data for the futurefor training of correctly identifying an IP address. However, aftercomparing the data structure of the data item 203 a, the server 210 adetermines that the data structure of the data item 203 a does notconform to the comparison dataset 208 a, then the server 210 a uses theexample training scenario as training data for future training to showwhen an IP address is incorrectly identified.

In other embodiments, the server 210 a compares the data structure ofthe data item 205 a and determines whether the data structure of thedata item 205 a conforms to the comparison dataset 208 a. If not, theserver 210 a uses the example training scenario as training data forfuture training to correctly identify Non-IP address data. However,after comparing the data structure of the data item 205 a, the server210 a determines that the data structure of the data item 205 a does notconform to the comparison dataset 208 a, then the server 210 a uses theexample training scenario as training data for future training to showwhen Non-IP address data is correctly identified.

In further embodiments, the neural network 206 a can indicate theprobability of certainty (72%) 211 a that it believes it correctlyidentified a data item 203 a as an IP address and indicate theprobability of certainty (81%) 215 a that it believes it correctlyidentified a data item 205 a as Non-IP address data. In additionalembodiments, the neural network 206 a can be trained until theprobability of certainty is above a threshold (e.g., 90%). Thus, in theexample shown in FIG. 2E, the probability of certainty for correctlyidentifying an IP address and Non-IP address data is each less than 90%such that the neural network 206 a may be further trained to increaseits probability certainty in correctly identifying both IP address andNon-IP address data prior to being deployed for use. In someembodiments, the threshold for the probably of certainty to correctlyidentify an IP address can be a same value as the threshold for theprobably of certainty to correctly identify Non-IP address data (e.g.,90%). In other embodiments, the threshold for the probably of certaintyto correctly identify an IP address can be a different value as thethreshold for the probably of certainty to correctly identify Non-IPaddress data.

In further embodiments, instead of, or in addition to, the server 210 acomparing data items 203 a, 205 a to a comparison dataset 208 a, a user214 a (such as company personnel) can access the data item 203 a anddata item 205 a and provide user-generated input to the server 210 a toindicate whether data item 203 a has been correctly identified by theneural network 206 a as an IP address and/or whether data item 205 a hasbeen correctly identified by the neural network 206 a as Non-IP addressdata.

For example, a data item 203 a can be a phone number that the neuralnetwork 206 a indicates to be an IP address with a probability certaintyof 72% 211 a and a data item 205 a can be a different phone number thatthe neural network 206 a indicates to be Non-IP address data with aprobability certainty of 81% 215 a. In some embodiments, the server 210a compares data item 203 a to the comparison dataset 208 a anddetermines that data item 203 a is incorrectly identified as an IPaddress. In other embodiments, the server 210 a compares data item 205 ato the comparison dataset 208 a and determines that data item 205 a iscorrectly identified as Non-IP address data. In further embodiments, theserver 210 a can provide data item 203 a and data item 205 a (along withan indication on the identification of the data items by the neuralnetwork on whether they each have been identified as IP address orNon-IP address data, respectively) to the computing device 212 a forviewing by user 214 a. The user 214 a can provide, through computingdevice 212 a, user-generated input that indicates whether data item 203a has been correctly identified as an IP address and whether data item205 a has been correctly identified as Non-IP address data. For example,user 214 a can provide user-generated input that indicates data item 203a has been incorrectly identified as an IP address and provideuser-generated input that indicates data item 205 a has been correctlyidentified as Non-IP address data.

In another example, a data item 207 a from the training data 202 a canbe an IP address that the neural network 206 aa correctly indicates tobe an IP address with a probability certainty of 92% 217 a and a dataitem 209 a from the training data 202 a can be a phone number that theneural network 206 aa indicates to be Non-IP address data with aprobability certainty of 96% 219 a. In some embodiments, the server 210a compares data item 207 a to the comparison dataset 208 a anddetermines that data item 207 a is correctly identified as an IPaddress. In other embodiments, the server 210 a compares data item 209 ato the comparison dataset 208 a and determines that data item 209 a iscorrectly identified as Non-IP address data. In further embodiments, theserver 210 a can provide data item 207 a and data item 209 a (along withan indication on the identification of the data items by the neuralnetwork on whether they each are IP address or Non-IP address data) tothe computing device 212 a for viewing by user 214 a. The user 214 a canprovide, through computing device 212 a, user-generated input thatindicates whether data item 207 a has been correctly identified as an IPaddress and whether data item 209 a has been correctly identified asNon-IP address data. Further, the probably of certainty that the neuralnetwork 206 aa to correctly identify both the IP address and Non-IPaddress data is above a threshold (e.g., 90%) such that the neuralnetwork 206 aa may no longer be trained and deployed for use.

In one or more embodiments, three data sets from a data pool can bereserved for training, validation, and testing purposes. In someembodiments, the training data set can be used as training data 204 inFIG. 2A to feed into neural network 224 and flow through its hiddenlayers 226, 228, 230 in FIG. 2B such output layer 232 of the neuralnetwork 224 either recognizes either an IP address 234, or non-IPaddress data 236.

In further embodiments, the validation data set can be used to calculateloss and accuracy. Different algorithms can be used to fine tune neuralnetwork and one of these algorithms can be selected according to itshighest accuracy as the model. In additional embodiments, the test dataset, which is unknown to neural network, and run through the neuralnetwork as part of training data 204 in FIG. 2A and to determine it hasrecognized as IP address stored in 209 or non-IP address. Datarecognized as IPv4 address is stored in 208, while IPv6 is stored in 210in FIG. 2A.

In one or more embodiments, different network algorithms have differentstrengths to handle different types of data 202 a in FIG. 2E withcharacteristic such as IP address, email address, etc. Different modelsare selected based on the highest accuracy rate during training andvalidation phases, so when it is used in the testing phase, a betteraccuracy outcome can be achieved.

In one or more embodiments, once a fine tuned neural network 206 aa inFIG. 2E is achieved, data which was not used in training and validationphases on 206 aa can be analyzed and split (categorized, identified,etc.) into 217 a IP address related data and 219 a non-IP address data.Server 202 in FIG. 2A can store these IP address data 217A into 209.Further, neural network 206 aa can split (categorize, identify, etc.)217 a data into IPv4 and IPv6 data. In additional embodiments, once amature neural network is found and being used in 206 aa, if any update(add/modify/delete) of data (IP address or non-IP address data) to datapool 206 in FIG. 2A or 206 b in FIG. 2H, server 202 in FIG. 2A does notneed to train neural network anymore. Embodiments can keep recognized IPaddress data in database 210 b in FIG. 2H up-to-date so it can be usedin run time.

FIGS. 2F-G depicts illustrative embodiments of methods in accordancewith various aspects described herein. The methods shown in FIGS. 2F-Gare directed to training a neural network. Referring to FIG. 2F, in oneor more embodiments, the method 220 a can be implemented by a server asshown in FIGS. 2A and 2E. The method 220 a includes the server, at 222a, providing training data to the neural network (data from data poolcan be provided to neural network 206 aa in FIG. 2E, and the data can beidentified as IP address data or non-IP address data, as in steps 224 aor 224 b). Further, the method 220 a includes the server, at 224 a,determining a data item from the training data is an IP address by theneural network. In addition, the method 220 a includes the server, at226 a, determining the probability of certainty that the data item iscorrectly identified as an IP address by the neural network. Also, themethod 220 a includes the server, at 228 a, comparing the data item tothe comparison dataset. Further, the method 220 a includes the server,at 230 a, determining the data item has not correctly been identified asan IP address based on the comparing of the data item to the comparisondataset. In addition, the method 220 a includes the server, at 232 a,providing the training scenario into the training data for futuretraining of the neural network. Also, the method 220 a includes theserver, at 234 a, determining that the probability of certainty ofcorrectly identifying the data item as an IP address is below athreshold, and the method 220 a includes the server, at 236 a,determining that the neural network needs to be further trained toimprove the probability of certainty to be above the threshold. Such adetermination can be made based on whether the neural network correctlyidentified the data item as an IP address and/or whether the probably ofcertainty of correctly identifying the data item as an IP address isless than a threshold.

In one or more embodiments, the method 220 a includes the server, at 222a, providing training data to the neural network. Further, the method220 a includes the server, at 224 b, determining a data item from thetraining data is Non-IP address data by the neural network. In addition,the method 220 a includes the server, at 226 b, determining theprobability of certainty that the data item is correctly identified asNon-IP address data by the neural network. Also, the method 220 aincludes the server, at 228 b, comparing the data item to the comparisondataset. Further, the method 220 a includes the server, at 230 b,determining the data item has not correctly been identified as Non-IPaddress data based on the comparing of the data item to the comparisondataset. In addition, the method 220 a includes the server, at 232 b,providing the training scenario into the training data for futuretraining of the neural network. Also, the method 220 a includes theserver, at 234 b, determining that the probability of certainty ofcorrectly identifying the data item as Non-IP address data is below athreshold, and the method 220 a includes the server, at 236 a,determining that the neural network needs to be further trained toimprove the probability of certainty to be above the threshold. Such adetermination can be made based on whether the neural network correctlyidentified the data item as a Non-IP address data and/or whether theprobably of certainty of correctly identifying the data item as Non-IPaddress data is less than a threshold.

Referring to FIG. 2G, in one or more embodiments, the method 240 a canbe implemented by a server as shown in FIGS. 2A and 2E. The method 240 aincludes the server, at 242 a, providing training data to the neuralnetwork. Further, the method 240 a includes the server, at 244 a,determining a data item from the training data is an IP address by theneural network. In addition, the method 240 a includes the server, at246 a, determining the probability of certainty that the data item iscorrectly identified as an IP address by the neural network. Also, themethod 240 a includes the server, at 248 a, comparing the data item tothe comparison dataset. Further, the method 240 a includes the server,at 250 a, determining the data item has correctly been identified as anIP address based on the comparing of the data item to the comparisondataset. In addition, the method 240 a includes the server, at 252 a,providing the training scenario into the training data for futuretraining of the neural network. Also, the method 240 a includes theserver, at 254 a, determining that the probability of certainty ofcorrectly identifying the data item as an IP address is above athreshold, and the method 240 a includes the server, at 256 a,determining that the neural network may not need to be further trainedto improve the probability of certainty to be above the threshold. Sucha determination can be made based on whether the neural networkcorrectly identified the data item as an IP address and/or whether theprobably of certainty of correctly identifying the data item as an IPaddress is above a threshold.

In one or more embodiments, the method 240 a includes the server, at 242a, providing training data to the neural network. Further, the method240 a includes the server, at 244 b, determining a data item from thetraining data is Non-IP address data by the neural network. In addition,the method 240 a includes the server, at 246 b, determining theprobability of certainty that the data item is correctly identified asNon-IP address data by the neural network. Also, the method 240 aincludes the server, at 248 b, comparing the data item to the comparisondataset. Further, the method 240 a includes the server, at 250 b,determining the data item has correctly been identified as Non-IPaddress data based on the comparing of the data item to the comparisondataset. In addition, the method 240 a includes the server, at 252 b,providing the training scenario into the training data for futuretraining of the neural network. Also, the method 240 a includes theserver, at 254 b, determining that the probability of certainty ofcorrectly identifying the data item as Non-IP address data is above athreshold, and the method 240 a includes the server, at 256 a,determining that the neural network may not need to be further trainedto improve the probability of certainty to be above the threshold. Sucha determination can be made based on whether the neural networkcorrectly identified the data item as Non-IP address and/or whether theprobably of certainty of correctly identifying the data item as Non-IPaddress is above a threshold.

While for purposes of simplicity of explanation, the respectiveprocesses are shown and described as a series of blocks in FIGS. 2C-Dand FIGS. 2F-G, it is to be understood and appreciated that the claimedsubject matter is not limited by the order of the blocks, as some blocksmay occur in different orders and/or concurrently with other blocks fromwhat is depicted and described herein. Moreover, not all illustratedblocks may be required to implement the methods described herein.

In addition, portions of some embodiments described herein can becombined with portions of other embodiments described herein.

Although embodiments described herein are directed to a neural networkrecognizing or identifying an IP address from a mixed data pool, otherembodiments can include using a neural network recognizing oridentifying other types of data that can include, but not limited to,telephone number, physical address, location coordinates, location data,email address, persons names, or data with a specific data structure, orany other data.

Referring now to FIG. 3, a block diagram 300 is shown illustrating anexample, non-limiting embodiment of a virtualized communication networkin accordance with various aspects described herein. In particular avirtualized communication network is presented that can be used toimplement some or all of the subsystems and functions of communicationnetwork 100, the subsystems and functions of systems 200, 220 and method240, 260 presented in FIGS. 1, 2A, 2B, 2C, 2D and 3. For example,virtualized communication network 300 can facilitate in whole or in partidentifying an IP address from a pool of different types of data thatinclude a group of IP addresses and determining whether the IP addressis currently being used by a computing device.

In particular, a cloud networking architecture is shown that leveragescloud technologies and supports rapid innovation and scalability via atransport layer 350, a virtualized network function cloud 325 and/or oneor more cloud computing environments 375. In various embodiments, thiscloud networking architecture is an open architecture that leveragesapplication programming interfaces (APIs); reduces complexity fromservices and operations; supports more nimble business models; andrapidly and seamlessly scales to meet evolving customer requirementsincluding traffic growth, diversity of traffic types, and diversity ofperformance and reliability expectations.

In contrast to traditional network elements—which are typicallyintegrated to perform a single function, the virtualized communicationnetwork employs virtual network elements (VNEs) 330, 332, 334, etc. thatperform some or all of the functions of network elements 150, 152, 154,156, etc. For example, the network architecture can provide a substrateof networking capability, often called Network Function VirtualizationInfrastructure (NFVI) or simply infrastructure that is capable of beingdirected with software and Software Defined Networking (SDN) protocolsto perform a broad variety of network functions and services. Thisinfrastructure can include several types of substrates. The most typicaltype of substrate being servers that support Network FunctionVirtualization (NFV), followed by packet forwarding capabilities basedon generic computing resources, with specialized network technologiesbrought to bear when general purpose processors or general purposeintegrated circuit devices offered by merchants (referred to herein asmerchant silicon) are not appropriate. In this case, communicationservices can be implemented as cloud-centric workloads.

As an example, a traditional network element 150 (shown in FIG. 1), suchas an edge router can be implemented via a VNE 330 composed of NFVsoftware modules, merchant silicon, and associated controllers. Thesoftware can be written so that increasing workload consumes incrementalresources from a common resource pool, and moreover so that it'selastic: so the resources are only consumed when needed. In a similarfashion, other network elements such as other routers, switches, edgecaches, and middle-boxes are instantiated from the common resource pool.Such sharing of infrastructure across a broad set of uses makes planningand growing infrastructure easier to manage.

In an embodiment, the transport layer 350 includes fiber, cable, wiredand/or wireless transport elements, network elements and interfaces toprovide broadband access 110, wireless access 120, voice access 130,media access 140 and/or access to content sources 175 for distributionof content to any or all of the access technologies. In particular, insome cases a network element needs to be positioned at a specific place,and this allows for less sharing of common infrastructure. Other times,the network elements have specific physical layer adapters that cannotbe abstracted or virtualized, and might require special DSP code andanalog front-ends (AFEs) that do not lend themselves to implementationas VNEs 330, 332 or 334. These network elements can be included intransport layer 350.

The virtualized network function cloud 325 interfaces with the transportlayer 350 to provide the VNEs 330, 332, 334, etc. to provide specificNFVs. In particular, the virtualized network function cloud 325leverages cloud operations, applications, and architectures to supportnetworking workloads. The virtualized network elements 330, 332 and 334can employ network function software that provides either a one-for-onemapping of traditional network element function or alternately somecombination of network functions designed for cloud computing. Forexample, VNEs 330, 332 and 334 can include route reflectors, domain namesystem (DNS) servers, and dynamic host configuration protocol (DHCP)servers, system architecture evolution (SAE) and/or mobility managemententity (MME) gateways, broadband network gateways, IP edge routers forIP-VPN, Ethernet and other services, load balancers, distributers andother network elements. Because these elements don't typically need toforward large amounts of traffic, their workload can be distributedacross a number of servers—each of which adds a portion of thecapability, and overall which creates an elastic function with higheravailability than its former monolithic version. These virtual networkelements 330, 332, 334, etc. can be instantiated and managed using anorchestration approach similar to those used in cloud compute services.

The cloud computing environments 375 can interface with the virtualizednetwork function cloud 325 via APIs that expose functional capabilitiesof the VNEs 330, 332, 334, etc. to provide the flexible and expandedcapabilities to the virtualized network function cloud 325. Inparticular, network workloads may have applications distributed acrossthe virtualized network function cloud 325 and cloud computingenvironment 375 and in the commercial cloud, or might simply orchestrateworkloads supported entirely in NFV infrastructure from these thirdparty locations.

Turning now to FIG. 4, there is illustrated a block diagram of acomputing environment in accordance with various aspects describedherein. In order to provide additional context for various embodimentsof the embodiments described herein, FIG. 4 and the following discussionare intended to provide a brief, general description of a suitablecomputing environment 400 in which the various embodiments of thesubject disclosure can be implemented. In particular, computingenvironment 400 can be used in the implementation of network elements150, 152, 154, 156, access terminal 112, base station or access point122, switching device 132, media terminal 142, and/or VNEs 330, 332,334, etc. Each of these devices can be implemented viacomputer-executable instructions that can run on one or more computers,and/or in combination with other program modules and/or as a combinationof hardware and software. For example, computing environment 400 canfacilitate in whole or in part an identifying IP address from a pool ofdifferent types of data that include a group of IP addresses anddetermining whether the IP address is currently being used by acomputing device. The servers, databases, information repositories, andcomputing devices described herein can comprise the computingenvironment 400.

Generally, program modules comprise routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will appreciatethat the methods can be practiced with other computer systemconfigurations, comprising single-processor or multiprocessor computersystems, minicomputers, mainframe computers, as well as personalcomputers, hand-held computing devices, microprocessor-based orprogrammable consumer electronics, and the like, each of which can beoperatively coupled to one or more associated devices.

As used herein, a processing circuit includes one or more processors aswell as other application specific circuits such as an applicationspecific integrated circuit, digital logic circuit, state machine,programmable gate array or other circuit that processes input signals ordata and that produces output signals or data in response thereto. Itshould be noted that while any functions and features described hereinin association with the operation of a processor could likewise beperformed by a processing circuit.

The illustrated embodiments of the embodiments herein can be alsopracticed in distributed computing environments where certain tasks areperformed by remote processing devices that are linked through acommunications network. In a distributed computing environment, programmodules can be located in both local and remote memory storage devices.

Computing devices typically comprise a variety of media, which cancomprise computer-readable storage media and/or communications media,which two terms are used herein differently from one another as follows.Computer-readable storage media can be any available storage media thatcan be accessed by the computer and comprises both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media can be implementedin connection with any method or technology for storage of informationsuch as computer-readable instructions, program modules, structured dataor unstructured data.

Computer-readable storage media can comprise, but are not limited to,random access memory (RAM), read only memory (ROM), electricallyerasable programmable read only memory (EEPROM), flash memory or othermemory technology, compact disk read only memory (CD-ROM), digitalversatile disk (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devicesor other tangible and/or non-transitory media which can be used to storedesired information. In this regard, the terms “tangible” or“non-transitory” herein as applied to storage, memory orcomputer-readable media, are to be understood to exclude onlypropagating transitory signals per se as modifiers and do not relinquishrights to all standard storage, memory or computer-readable media thatare not only propagating transitory signals per se.

Computer-readable storage media can be accessed by one or more local orremote computing devices, e.g., via access requests, queries or otherdata retrieval protocols, for a variety of operations with respect tothe information stored by the medium.

Communications media typically embody computer-readable instructions,data structures, program modules or other structured or unstructureddata in a data signal such as a modulated data signal, e.g., a carrierwave or other transport mechanism, and comprises any informationdelivery or transport media. The term “modulated data signal” or signalsrefers to a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in one or moresignals. By way of example, and not limitation, communication mediacomprise wired media, such as a wired network or direct-wiredconnection, and wireless media such as acoustic, RF, infrared and otherwireless media.

With reference again to FIG. 4, the example environment can comprise acomputer 402, the computer 402 comprising a processing unit 404, asystem memory 406 and a system bus 408. The system bus 408 couplessystem components including, but not limited to, the system memory 406to the processing unit 404. The processing unit 404 can be any ofvarious commercially available processors. Dual microprocessors andother multiprocessor architectures can also be employed as theprocessing unit 404.

The system bus 408 can be any of several types of bus structure that canfurther interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures.

The system memory 406 comprises ROM 410 and RAM 412. A basicinput/output system (BIOS) can be stored in a non-volatile memory suchas ROM, erasable programmable read only memory (EPROM), EEPROM, whichBIOS contains the basic routines that help to transfer informationbetween elements within the computer 402, such as during startup. TheRAM 412 can also comprise a high-speed RAM such as static RAM forcaching data.

The computer 402 further comprises an internal hard disk drive (HDD) 414(e.g., EIDE, SATA), which internal HDD 414 can also be configured forexternal use in a suitable chassis (not shown), a magnetic floppy diskdrive (FDD) 416, (e.g., to read from or write to a removable diskette418) and an optical disk drive 420, (e.g., reading a CD-ROM disk 422 or,to read from or write to other high capacity optical media such as theDVD). The HDD 414, magnetic FDD 416 and optical disk drive 420 can beconnected to the system bus 408 by a hard disk drive interface 424, amagnetic disk drive interface 426 and an optical drive interface 428,respectively. The hard disk drive interface 424 for external driveimplementations comprises at least one or both of Universal Serial Bus(USB) and Institute of Electrical and Electronics Engineers (IEEE) 1394interface technologies. Other external drive connection technologies arewithin contemplation of the embodiments described herein.

The drives and their associated computer-readable storage media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 402, the drives and storagemedia accommodate the storage of any data in a suitable digital format.Although the description of computer-readable storage media above refersto a hard disk drive (HDD), a removable magnetic diskette, and aremovable optical media such as a CD or DVD, it should be appreciated bythose skilled in the art that other types of storage media which arereadable by a computer, such as zip drives, magnetic cassettes, flashmemory cards, cartridges, and the like, can also be used in the exampleoperating environment, and further, that any such storage media cancontain computer-executable instructions for performing the methodsdescribed herein.

A number of program modules can be stored in the drives and RAM 412,comprising an operating system 430, one or more application programs432, other program modules 434 and program data 436. All or portions ofthe operating system, applications, modules, and/or data can also becached in the RAM 412. The systems and methods described herein can beimplemented utilizing various commercially available operating systemsor combinations of operating systems.

A user can enter commands and information into the computer 402 throughone or more wired/wireless input devices, e.g., a keyboard 438 and apointing device, such as a mouse 440. Other input devices (not shown)can comprise a microphone, an infrared (IR) remote control, a joystick,a game pad, a stylus pen, touch screen or the like. These and otherinput devices are often connected to the processing unit 404 through aninput device interface 442 that can be coupled to the system bus 408,but can be connected by other interfaces, such as a parallel port, anIEEE 1394 serial port, a game port, a universal serial bus (USB) port,an IR interface, etc.

A monitor 444 or other type of display device can be also connected tothe system bus 408 via an interface, such as a video adapter 446. Itwill also be appreciated that in alternative embodiments, a monitor 444can also be any display device (e.g., another computer having a display,a smart phone, a tablet computer, etc.) for receiving displayinformation associated with computer 402 via any communication means,including via the Internet and cloud-based networks. In addition to themonitor 444, a computer typically comprises other peripheral outputdevices (not shown), such as speakers, printers, etc.

The computer 402 can operate in a networked environment using logicalconnections via wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 448. The remotecomputer(s) 448 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentappliance, a peer device or other common network node, and typicallycomprises many or all of the elements described relative to the computer402, although, for purposes of brevity, only a remote memory/storagedevice 450 is illustrated. The logical connections depicted comprisewired/wireless connectivity to a local area network (LAN) 452 and/orlarger networks, e.g., a wide area network (WAN) 454. Such LAN and WANnetworking environments are commonplace in offices and companies, andfacilitate enterprise-wide computer networks, such as intranets, all ofwhich can connect to a global communications network, e.g., theInternet.

When used in a LAN networking environment, the computer 402 can beconnected to the LAN 452 through a wired and/or wireless communicationnetwork interface or adapter 456. The adapter 456 can facilitate wiredor wireless communication to the LAN 452, which can also comprise awireless AP disposed thereon for communicating with the adapter 456.

When used in a WAN networking environment, the computer 402 can comprisea modem 458 or can be connected to a communications server on the WAN454 or has other means for establishing communications over the WAN 454,such as by way of the Internet. The modem 458, which can be internal orexternal and a wired or wireless device, can be connected to the systembus 408 via the input device interface 442. In a networked environment,program modules depicted relative to the computer 402 or portionsthereof, can be stored in the remote memory/storage device 450. It willbe appreciated that the network connections shown are example and othermeans of establishing a communications link between the computers can beused.

The computer 402 can be operable to communicate with any wirelessdevices or entities operatively disposed in wireless communication,e.g., a printer, scanner, desktop and/or portable computer, portabledata assistant, communications satellite, any piece of equipment orlocation associated with a wirelessly detectable tag (e.g., a kiosk,news stand, restroom), and telephone. This can comprise WirelessFidelity (Wi-Fi) and BLUETOOTH® wireless technologies. Thus, thecommunication can be a predefined structure as with a conventionalnetwork or simply an ad hoc communication between at least two devices.

Wi-Fi can allow connection to the Internet from a couch at home, a bedin a hotel room or a conference room at work, without wires. Wi-Fi is awireless technology similar to that used in a cell phone that enablessuch devices, e.g., computers, to send and receive data indoors and out;anywhere within the range of a base station. Wi-Fi networks use radiotechnologies called IEEE 802.11 (a, b, g, n, ac, ag, etc.) to providesecure, reliable, fast wireless connectivity. A Wi-Fi network can beused to connect computers to each other, to the Internet, and to wirednetworks (which can use IEEE 802.3 or Ethernet). Wi-Fi networks operatein the unlicensed 2.4 and 5 GHz radio bands for example or with productsthat contain both bands (dual band), so the networks can providereal-world performance similar to the basic 10BaseT wired Ethernetnetworks used in many offices.

Turning now to FIG. 5, an embodiment 500 of a mobile network platform510 is shown that is an example of network elements 150, 152, 154, 156,and/or VNEs 330, 332, 334, etc. For example, platform 510 can facilitatein whole or in part identifying an IP address from a pool of differenttypes of data that include a group of IP addresses and determiningwhether the IP address is currently being used by a computing device. Inone or more embodiments, the mobile network platform 510 can generateand receive signals transmitted and received by base stations or accesspoints such as base station or access point 122. Generally, mobilenetwork platform 510 can comprise components, e.g., nodes, gateways,interfaces, servers, or disparate platforms, that facilitate bothpacket-switched (PS) (e.g., internet protocol (IP), frame relay,asynchronous transfer mode (ATM)) and circuit-switched (CS) traffic(e.g., voice and data), as well as control generation for networkedwireless telecommunication. As a non-limiting example, mobile networkplatform 510 can be included in telecommunications carrier networks, andcan be considered carrier-side components as discussed elsewhere herein.Mobile network platform 510 comprises CS gateway node(s) 512 which caninterface CS traffic received from legacy networks like telephonynetwork(s) 540 (e.g., public switched telephone network (PSTN), orpublic land mobile network (PLMN)) or a signaling system #7 (SS7)network 560. CS gateway node(s) 512 can authorize and authenticatetraffic (e.g., voice) arising from such networks. Additionally, CSgateway node(s) 512 can access mobility, or roaming, data generatedthrough SS7 network 560; for instance, mobility data stored in a visitedlocation register (VLR), which can reside in memory 530. Moreover, CSgateway node(s) 512 interfaces CS-based traffic and signaling and PSgateway node(s) 518. As an example, in a 3GPP UMTS network, CS gatewaynode(s) 512 can be realized at least in part in gateway GPRS supportnode(s) (GGSN). It should be appreciated that functionality and specificoperation of CS gateway node(s) 512, PS gateway node(s) 518, and servingnode(s) 516, is provided and dictated by radio technology(ies) utilizedby mobile network platform 510 for telecommunication over a radio accessnetwork 520 with other devices, such as a radiotelephone 575.

In addition to receiving and processing CS-switched traffic andsignaling, PS gateway node(s) 518 can authorize and authenticatePS-based data sessions with served mobile devices. Data sessions cancomprise traffic, or content(s), exchanged with networks external to themobile network platform 510, like wide area network(s) (WANs) 550,enterprise network(s) 570, and service network(s) 580, which can beembodied in local area network(s) (LANs), can also be interfaced withmobile network platform 510 through PS gateway node(s) 518. It is to benoted that WANs 550 and enterprise network(s) 570 can embody, at leastin part, a service network(s) like IP multimedia subsystem (IMS). Basedon radio technology layer(s) available in technology resource(s) orradio access network 520, PS gateway node(s) 518 can generate packetdata protocol contexts when a data session is established; other datastructures that facilitate routing of packetized data also can begenerated. To that end, in an aspect, PS gateway node(s) 518 cancomprise a tunnel interface (e.g., tunnel termination gateway (TTG) in3GPP UMTS network(s) (not shown)) which can facilitate packetizedcommunication with disparate wireless network(s), such as Wi-Finetworks.

In embodiment 500, mobile network platform 510 also comprises servingnode(s) 516 that, based upon available radio technology layer(s) withintechnology resource(s) in the radio access network 520, convey thevarious packetized flows of data streams received through PS gatewaynode(s) 518. It is to be noted that for technology resource(s) that relyprimarily on CS communication, server node(s) can deliver trafficwithout reliance on PS gateway node(s) 518; for example, server node(s)can embody at least in part a mobile switching center. As an example, ina 3GPP UMTS network, serving node(s) 516 can be embodied in serving GPRSsupport node(s) (SGSN).

For radio technologies that exploit packetized communication, server(s)514 in mobile network platform 510 can execute numerous applicationsthat can generate multiple disparate packetized data streams or flows,and manage (e.g., schedule, queue, format . . . ) such flows. Suchapplication(s) can comprise add-on features to standard services (forexample, provisioning, billing, customer support . . . ) provided bymobile network platform 510. Data streams (e.g., content(s) that arepart of a voice call or data session) can be conveyed to PS gatewaynode(s) 518 for authorization/authentication and initiation of a datasession, and to serving node(s) 516 for communication thereafter. Inaddition to application server, server(s) 514 can comprise utilityserver(s), a utility server can comprise a provisioning server, anoperations and maintenance server, a security server that can implementat least in part a certificate authority and firewalls as well as othersecurity mechanisms, and the like. In an aspect, security server(s)secure communication served through mobile network platform 510 toensure network's operation and data integrity in addition toauthorization and authentication procedures that CS gateway node(s) 512and PS gateway node(s) 518 can enact. Moreover, provisioning server(s)can provision services from external network(s) like networks operatedby a disparate service provider; for instance, WAN 550 or GlobalPositioning System (GPS) network(s) (not shown). Provisioning server(s)can also provision coverage through networks associated to mobilenetwork platform 510 (e.g., deployed and operated by the same serviceprovider), such as the distributed antennas networks shown in FIG. 1(s)that enhance wireless service coverage by providing more networkcoverage.

It is to be noted that server(s) 514 can comprise one or more processorsconfigured to confer at least in part the functionality of mobilenetwork platform 510. To that end, the one or more processor can executecode instructions stored in memory 530, for example. It is should beappreciated that server(s) 514 can comprise a content manager, whichoperates in substantially the same manner as described hereinbefore.

In example embodiment 500, memory 530 can store information related tooperation of mobile network platform 510. Other operational informationcan comprise provisioning information of mobile devices served throughmobile network platform 510, subscriber databases; applicationintelligence, pricing schemes, e.g., promotional rates, flat-rateprograms, couponing campaigns; technical specification(s) consistentwith telecommunication protocols for operation of disparate radio, orwireless, technology layers; and so forth. Memory 530 can also storeinformation from at least one of telephony network(s) 540, WAN 550, SS7network 560, or enterprise network(s) 570. In an aspect, memory 530 canbe, for example, accessed as part of a data store component or as aremotely connected memory store.

In order to provide a context for the various aspects of the disclosedsubject matter, FIG. 5, and the following discussion, are intended toprovide a brief, general description of a suitable environment in whichthe various aspects of the disclosed subject matter can be implemented.While the subject matter has been described above in the general contextof computer-executable instructions of a computer program that runs on acomputer and/or computers, those skilled in the art will recognize thatthe disclosed subject matter also can be implemented in combination withother program modules. Generally, program modules comprise routines,programs, components, data structures, etc. that perform particulartasks and/or implement particular abstract data types.

Turning now to FIG. 6, an illustrative embodiment of a communicationdevice 600 is shown. The communication device 600 can serve as anillustrative embodiment of devices such as data terminals 114, mobiledevices 124, vehicle 126, display devices 144 or other client devicesfor communication via either communications network 125. For example,computing device 600 can facilitate in whole or in part the embodimentsdescribed herein. The servers, databases, information repositories, andcomputing devices described herein can comprise the communication device600.

The communication device 600 can comprise a wireline and/or wirelesstransceiver 602 (herein transceiver 602), a user interface (UI) 604, apower supply 614, a location receiver 616, a motion sensor 618, anorientation sensor 620, and a controller 606 for managing operationsthereof. The transceiver 602 can support short-range or long-rangewireless access technologies such as Bluetooth®, ZigBee®, WiFi, DECT, orcellular communication technologies, just to mention a few (Bluetooth®and ZigBee® are trademarks registered by the Bluetooth® Special InterestGroup and the ZigBee® Alliance, respectively). Cellular technologies caninclude, for example, CDMA-1X, UMTS/HSDPA, GSM/GPRS, TDMA/EDGE, EV/DO,WiMAX, SDR, LTE, as well as other next generation wireless communicationtechnologies as they arise. The transceiver 602 can also be adapted tosupport circuit-switched wireline access technologies (such as PSTN),packet-switched wireline access technologies (such as TCP/IP, VoIP,etc.), and combinations thereof.

The UI 604 can include a depressible or touch-sensitive keypad 608 witha navigation mechanism such as a roller ball, a joystick, a mouse, or anavigation disk for manipulating operations of the communication device600. The keypad 608 can be an integral part of a housing assembly of thecommunication device 600 or an independent device operably coupledthereto by a tethered wireline interface (such as a USB cable) or awireless interface supporting for example Bluetooth®. The keypad 608 canrepresent a numeric keypad commonly used by phones, and/or a QWERTYkeypad with alphanumeric keys. The UI 604 can further include a display610 such as monochrome or color LCD (Liquid Crystal Display), OLED(Organic Light Emitting Diode) or other suitable display technology forconveying images to an end user of the communication device 600. In anembodiment where the display 610 is touch-sensitive, a portion or all ofthe keypad 608 can be presented by way of the display 610 withnavigation features.

The display 610 can use touch screen technology to also serve as a userinterface for detecting user input. As a touch screen display, thecommunication device 600 can be adapted to present a user interfacehaving graphical user interface (GUI) elements that can be selected by auser with a touch of a finger. The display 610 can be equipped withcapacitive, resistive or other forms of sensing technology to detect howmuch surface area of a user's finger has been placed on a portion of thetouch screen display. This sensing information can be used to controlthe manipulation of the GUI elements or other functions of the userinterface. The display 610 can be an integral part of the housingassembly of the communication device 600 or an independent devicecommunicatively coupled thereto by a tethered wireline interface (suchas a cable) or a wireless interface.

The UI 604 can also include an audio system 612 that utilizes audiotechnology for conveying low volume audio (such as audio heard inproximity of a human ear) and high volume audio (such as speakerphonefor hands free operation). The audio system 612 can further include amicrophone for receiving audible signals of an end user. The audiosystem 612 can also be used for voice recognition applications. The UI604 can further include an image sensor 613 such as a charged coupleddevice (CCD) camera for capturing still or moving images.

The power supply 614 can utilize common power management technologiessuch as replaceable and rechargeable batteries, supply regulationtechnologies, and/or charging system technologies for supplying energyto the components of the communication device 600 to facilitatelong-range or short-range portable communications. Alternatively, or incombination, the charging system can utilize external power sources suchas DC power supplied over a physical interface such as a USB port orother suitable tethering technologies.

The location receiver 616 can utilize location technology such as aglobal positioning system (GPS) receiver capable of assisted GPS foridentifying a location of the communication device 600 based on signalsgenerated by a constellation of GPS satellites, which can be used forfacilitating location services such as navigation. The motion sensor 618can utilize motion sensing technology such as an accelerometer, agyroscope, or other suitable motion sensing technology to detect motionof the communication device 600 in three-dimensional space. Theorientation sensor 620 can utilize orientation sensing technology suchas a magnetometer to detect the orientation of the communication device600 (north, south, west, and east, as well as combined orientations indegrees, minutes, or other suitable orientation metrics).

The communication device 600 can use the transceiver 602 to alsodetermine a proximity to a cellular, WiFi, Bluetooth®, or other wirelessaccess points by sensing techniques such as utilizing a received signalstrength indicator (RSSI) and/or signal time of arrival (TOA) or time offlight (TOF) measurements. The controller 606 can utilize computingtechnologies such as a microprocessor, a digital signal processor (DSP),programmable gate arrays, application specific integrated circuits,and/or a video processor with associated storage memory such as Flash,ROM, RAM, SRAM, DRAM or other storage technologies for executingcomputer instructions, controlling, and processing data supplied by theaforementioned components of the communication device 600.

Other components not shown in FIG. 6 can be used in one or moreembodiments of the subject disclosure. For instance, the communicationdevice 600 can include a slot for adding or removing an identity modulesuch as a Subscriber Identity Module (SIM) card or Universal IntegratedCircuit Card (UICC). SIM or UICC cards can be used for identifyingsubscriber services, executing programs, storing subscriber data, and soon.

The terms “first,” “second,” “third,” and so forth, as used in theclaims, unless otherwise clear by context, is for clarity only anddoesn't otherwise indicate or imply any order in time. For instance, “afirst determination,” “a second determination,” and “a thirddetermination,” does not indicate or imply that the first determinationis to be made before the second determination, or vice versa, etc.

In the subject specification, terms such as “store,” “storage,” “datastore,” data storage,” “database,” and substantially any otherinformation storage component relevant to operation and functionality ofa component, refer to “memory components,” or entities embodied in a“memory” or components comprising the memory. It will be appreciatedthat the memory components described herein can be either volatilememory or nonvolatile memory, or can comprise both volatile andnonvolatile memory, by way of illustration, and not limitation, volatilememory, non-volatile memory, disk storage, and memory storage. Further,nonvolatile memory can be included in read only memory (ROM),programmable ROM (PROM), electrically programmable ROM (EPROM),electrically erasable ROM (EEPROM), or flash memory. Volatile memory cancomprise random access memory (RAM), which acts as external cachememory. By way of illustration and not limitation, RAM is available inmany forms such as synchronous RAM (SRAM), dynamic RAM (DRAM),synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhancedSDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct Rambus RAM (DRRAM).Additionally, the disclosed memory components of systems or methodsherein are intended to comprise, without being limited to comprising,these and any other suitable types of memory.

Moreover, it will be noted that the disclosed subject matter can bepracticed with other computer system configurations, comprisingsingle-processor or multiprocessor computer systems, mini-computingdevices, mainframe computers, as well as personal computers, hand-heldcomputing devices (e.g., PDA, phone, smartphone, watch, tabletcomputers, netbook computers, etc.), microprocessor-based orprogrammable consumer or industrial electronics, and the like. Theillustrated aspects can also be practiced in distributed computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network; however, some if not allaspects of the subject disclosure can be practiced on stand-alonecomputers. In a distributed computing environment, program modules canbe located in both local and remote memory storage devices.

In one or more embodiments, information regarding use of services can begenerated including services being accessed, media consumption history,user preferences, and so forth. This information can be obtained byvarious methods including user input, detecting types of communications(e.g., video content vs. audio content), analysis of content streams,sampling, and so forth. The generating, obtaining and/or monitoring ofthis information can be responsive to an authorization provided by theuser. In one or more embodiments, an analysis of data can be subject toauthorization from user(s) associated with the data, such as an opt-in,an opt-out, acknowledgement requirements, notifications, selectiveauthorization based on types of data, and so forth.

Some of the embodiments described herein can also employ artificialintelligence (AI) to facilitate automating one or more featuresdescribed herein. The embodiments (e.g., in connection withautomatically identifying acquired cell sites that provide a maximumvalue/benefit after addition to an existing communication network) canemploy various AI-based schemes for carrying out various embodimentsthereof. Moreover, the classifier can be employed to determine a rankingor priority of each cell site of the acquired network. A classifier is afunction that maps an input attribute vector, x=(x1, x2, x3, x4, . . . ,xn), to a confidence that the input belongs to a class, that is,f(x)=confidence (class). Such classification can employ a probabilisticand/or statistical-based analysis (e.g., factoring into the analysisutilities and costs) to determine or infer an action that a user desiresto be automatically performed. A support vector machine (SVM) is anexample of a classifier that can be employed. The SVM operates byfinding a hypersurface in the space of possible inputs, which thehypersurface attempts to split the triggering criteria from thenon-triggering events. Intuitively, this makes the classificationcorrect for testing data that is near, but not identical to trainingdata. Other directed and undirected model classification approachescomprise, e.g., naïve Bayes, Bayesian networks, decision trees, neuralnetworks, fuzzy logic models, and probabilistic classification modelsproviding different patterns of independence can be employed.Classification as used herein also is inclusive of statisticalregression that is utilized to develop models of priority.

As will be readily appreciated, one or more of the embodiments canemploy classifiers that are explicitly trained (e.g., via a generictraining data) as well as implicitly trained (e.g., via observing UEbehavior, operator preferences, historical information, receivingextrinsic information). For example, SVMs can be configured via alearning or training phase within a classifier constructor and featureselection module. Thus, the classifier(s) can be used to automaticallylearn and perform a number of functions, including but not limited todetermining according to predetermined criteria which of the acquiredcell sites will benefit a maximum number of subscribers and/or which ofthe acquired cell sites will add minimum value to the existingcommunication network coverage, etc.

As used in some contexts in this application, in some embodiments, theterms “component,” “system” and the like are intended to refer to, orcomprise, a computer-related entity or an entity related to anoperational apparatus with one or more specific functionalities, whereinthe entity can be either hardware, a combination of hardware andsoftware, software, or software in execution. As an example, a componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution,computer-executable instructions, a program, and/or a computer. By wayof illustration and not limitation, both an application running on aserver and the server can be a component. One or more components mayreside within a process and/or thread of execution and a component maybe localized on one computer and/or distributed between two or morecomputers. In addition, these components can execute from variouscomputer readable media having various data structures stored thereon.The components may communicate via local and/or remote processes such asin accordance with a signal having one or more data packets (e.g., datafrom one component interacting with another component in a local system,distributed system, and/or across a network such as the Internet withother systems via the signal). As another example, a component can be anapparatus with specific functionality provided by mechanical partsoperated by electric or electronic circuitry, which is operated by asoftware or firmware application executed by a processor, wherein theprocessor can be internal or external to the apparatus and executes atleast a part of the software or firmware application. As yet anotherexample, a component can be an apparatus that provides specificfunctionality through electronic components without mechanical parts,the electronic components can comprise a processor therein to executesoftware or firmware that confers at least in part the functionality ofthe electronic components. While various components have beenillustrated as separate components, it will be appreciated that multiplecomponents can be implemented as a single component, or a singlecomponent can be implemented as multiple components, without departingfrom example embodiments.

Further, the various embodiments can be implemented as a method,apparatus or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer program accessible from anycomputer-readable device or computer-readable storage/communicationsmedia. For example, computer readable storage media can include, but arenot limited to, magnetic storage devices (e.g., hard disk, floppy disk,magnetic strips), optical disks (e.g., compact disk (CD), digitalversatile disk (DVD)), smart cards, and flash memory devices (e.g.,card, stick, key drive). Of course, those skilled in the art willrecognize many modifications can be made to this configuration withoutdeparting from the scope or spirit of the various embodiments.

In addition, the words “example” and “exemplary” are used herein to meanserving as an instance or illustration. Any embodiment or designdescribed herein as “example” or “exemplary” is not necessarily to beconstrued as preferred or advantageous over other embodiments ordesigns. Rather, use of the word example or exemplary is intended topresent concepts in a concrete fashion. As used in this application, theterm “or” is intended to mean an inclusive “or” rather than an exclusive“or”. That is, unless specified otherwise or clear from context, “Xemploys A or B” is intended to mean any of the natural inclusivepermutations. That is, if X employs A; X employs B; or X employs both Aand B, then “X employs A or B” is satisfied under any of the foregoinginstances. In addition, the articles “a” and “an” as used in thisapplication and the appended claims should generally be construed tomean “one or more” unless specified otherwise or clear from context tobe directed to a singular form.

Moreover, terms such as “user equipment,” “mobile station,” “mobile,”subscriber station,” “access terminal,” “terminal,” “handset,” “mobiledevice” (and/or terms representing similar terminology) can refer to awireless device utilized by a subscriber or user of a wirelesscommunication service to receive or convey data, control, voice, video,sound, gaming or substantially any data-stream or signaling-stream. Theforegoing terms are utilized interchangeably herein and with referenceto the related drawings.

Furthermore, the terms “user,” “subscriber,” “customer,” “consumer” andthe like are employed interchangeably throughout, unless contextwarrants particular distinctions among the terms. It should beappreciated that such terms can refer to human entities or automatedcomponents supported through artificial intelligence (e.g., a capacityto make inference based, at least, on complex mathematical formalisms),which can provide simulated vision, sound recognition and so forth.

As employed herein, the term “processor” can refer to substantially anycomputing processing unit or device comprising, but not limited tocomprising, single-core processors; single-processors with softwaremultithread execution capability; multi-core processors; multi-coreprocessors with software multithread execution capability; multi-coreprocessors with hardware multithread technology; parallel platforms; andparallel platforms with distributed shared memory. Additionally, aprocessor can refer to an integrated circuit, an application specificintegrated circuit (ASIC), a digital signal processor (DSP), a fieldprogrammable gate array (FPGA), a programmable logic controller (PLC), acomplex programmable logic device (CPLD), a discrete gate or transistorlogic, discrete hardware components or any combination thereof designedto perform the functions described herein. Processors can exploitnano-scale architectures such as, but not limited to, molecular andquantum-dot based transistors, switches and gates, in order to optimizespace usage or enhance performance of user equipment. A processor canalso be implemented as a combination of computing processing units.

As used herein, terms such as “data storage,” data storage,” “database,”and substantially any other information storage component relevant tooperation and functionality of a component, refer to “memorycomponents,” or entities embodied in a “memory” or components comprisingthe memory. It will be appreciated that the memory components orcomputer-readable storage media, described herein can be either volatilememory or nonvolatile memory or can include both volatile andnonvolatile memory.

What has been described above includes mere examples of variousembodiments. It is, of course, not possible to describe everyconceivable combination of components or methodologies for purposes ofdescribing these examples, but one of ordinary skill in the art canrecognize that many further combinations and permutations of the presentembodiments are possible. Accordingly, the embodiments disclosed and/orclaimed herein are intended to embrace all such alterations,modifications and variations that fall within the spirit and scope ofthe appended claims. Furthermore, to the extent that the term “includes”is used in either the detailed description or the claims, such term isintended to be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

As may also be used herein, the term(s) “operably coupled to”, “coupledto”, and/or “coupling” includes direct coupling between items and/orindirect coupling between items via one or more intervening items. Suchitems and intervening items include, but are not limited to, junctions,communication paths, components, circuit elements, circuits, functionalblocks, and/or devices. As an example of indirect coupling, a signalconveyed from a first item to a second item may be modified by one ormore intervening items by modifying the form, nature or format ofinformation in a signal, while one or more elements of the informationin the signal are nevertheless conveyed in a manner than can berecognized by the second item. In a further example of indirectcoupling, an action in a first item can cause a reaction on the seconditem, as a result of actions and/or reactions in one or more interveningitems.

Although specific embodiments have been illustrated and describedherein, it should be appreciated that any arrangement which achieves thesame or similar purpose may be substituted for the embodiments describedor shown by the subject disclosure. The subject disclosure is intendedto cover any and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, can be used in the subject disclosure.For instance, one or more features from one or more embodiments can becombined with one or more features of one or more other embodiments. Inone or more embodiments, features that are positively recited can alsobe negatively recited and excluded from the embodiment with or withoutreplacement by another structural and/or functional feature. The stepsor functions described with respect to the embodiments of the subjectdisclosure can be performed in any order. The steps or functionsdescribed with respect to the embodiments of the subject disclosure canbe performed alone or in combination with other steps or functions ofthe subject disclosure, as well as from other embodiments or from othersteps that have not been described in the subject disclosure. Further,more than or less than all of the features described with respect to anembodiment can also be utilized.

What is claimed is:
 1. A device, comprising: a processing systemincluding a processor; and a memory that stores executable instructionsthat, when executed by the processing system, facilitate performance ofoperations, the operations comprising: obtaining a first data pool ofdifferent types of data, wherein the first data pool comprises a firstgroup of IPv4 addresses and a second group of non-IP address data;identifying a first portion of the first group of IPv4 addresses from asecond portion of the second group of non-IP address data from withinthe first data pool using artificial intelligence techniques; selectingthe first portion of the first group of IPv4 addresses using theartificial intelligence techniques; storing the first portion of thefirst group of IPv4 addresses in a first table of IPv4 addresses; andassigning a first IPv4 address in the first table to a first computingdevice in response to determining the first IPv4 address in the firsttable is not currently being used.
 2. The device of claim 1, wherein thefirst data pool includes a third group of IPv6 addresses, wherein theoperations further comprise: identifying a third portion of the thirdgroup of IPv6 addresses from the second portion of the second group ofnon-IP address data from within the first data pool using the artificialintelligence techniques; selecting the third portion of the third groupof IPv6 addresses using the artificial intelligence techniques; andstoring the third portion of the third group of IPv6 addresses in asecond table of IPv6 addresses.
 3. The device of claim 2, wherein theoperations further comprise assigning a first IPv6 address in the secondtable to a second computing device in response to determining the firstIPv6 address in the second table is not currently being used.
 4. Thedevice of claim 2, wherein the operations further comprise removing asecond IPv6 address from the second table in response to determining thesecond IPv6 address in the second table is currently being used by athird computing device.
 5. The device of claim 2, wherein the operationsfurther comprise including the selecting of the third portion of thethird group of IPv6 addresses into training data.
 6. The device of claim1, wherein the operations further comprise removing a second IPv4address from the first table in response to determining the second IPv4address in the first table is currently being used by a fourth computingdevice.
 7. The device of claim 1, wherein the operations furthercomprise training the artificial intelligence techniques on trainingdata to recognize IPv4 addresses from a second data pool that includesthe different types of data.
 8. The device of claim 1, wherein theoperations further comprise training the artificial intelligencetechniques on training data to recognize IPv6 addresses from a thirddata pool that includes the different types of data.
 9. The device ofclaim 1, wherein the non-IP address data comprises of at least one ofpersonal identifiable information, name, physical address, telephonenumber, email address, location coordinates, location data, or acombination thereof.
 10. A non-transitory machine-readable medium,comprising executable instructions that, when executed by a processingsystem including a processor, facilitate performance of operations, theoperations comprising: obtaining a first data pool of different types ofdata, wherein the first data pool comprises a first group of IPv4addresses and a second group of non-IP address data; identifying a firstportion of the first group of IPv4 addresses from a second portion ofthe second group of non-IP address data from within the first data poolusing artificial intelligence techniques; selecting the first portion ofthe first group of IPv4 addresses using artificial intelligencetechniques; storing the first portion of the first group of IPv4addresses in a first table of IPv4 addresses; assigning a first IPv4address in the first table to a first computing device in response todetermining the first IPv4 address in the first table is not currentlybeing used; and removing a second IPv4 address from the first table inresponse to determining the second IPv4 address in the first table iscurrently being used by a second computing device.
 11. Themachine-readable medium of claim 10, wherein the operations furthercomprise training the artificial intelligence techniques on trainingdata to recognize IPv4 addresses from a second data pool that includesthe different types of data.
 12. The machine-readable medium of claim10, wherein the operations further comprise including the selecting ofthe first portion of the first group of IPv4 addresses into trainingdata.
 13. The machine-readable medium of claim 10, wherein the firstdata pool includes a third group of IPv6 addresses; wherein theoperations further comprise: identifying a third portion of the thirdgroup of IPv6 addresses from the second portion of the second group ofnon-IP address data from within the first data pool using the artificialintelligence techniques; selecting the third portion of the third groupof IPv6 addresses using the artificial intelligence techniques; andstoring the third portion of the third group of IPv6 addresses in asecond table of IPv6 addresses.
 14. The machine-readable medium of claim13, wherein the operations further comprise assigning a first IPv6address in the second table to a third computing device in response todetermining the first IPv6 address in the second table is not currentlybeing used.
 15. The machine-readable medium of claim 13, wherein theoperations further comprise removing a second IPv6 address from thesecond table in response to determining the second IPv6 address in thesecond table is currently being used by a fourth computing device. 16.The machine-readable medium of claim 13, wherein the operations furthercomprise including the selecting of the second portion of the secondgroup of IPv6 addresses into training data.
 17. The machine-readablemedium of claim 10, wherein the operations further comprise training theartificial intelligence techniques on training data to recognize IPv6addresses from a third data pool that includes the different types ofdata.
 18. A method, comprising: training, by a processing systemincluding a processor, artificial intelligence techniques on trainingdata to recognize IPv4 addresses from a first data pool that includesdifferent types of data; obtaining, by the processing system, a seconddata pool of the different types of data, wherein the second data poolcomprises a first group of addresses and a second group of non-IPaddress data; identifying, by the processing system, a first portion ofthe first group of IPv4 addresses from a second portion of the secondgroup of non-IP address data from with the second data pool using theartificial intelligence techniques; selecting, by the processing system,the first portion of the first group of IPv4 addresses using theartificial intelligence techniques; storing, by the processing system,the first portion of the first group of IPv4 addresses in a first tableof IPv4 addresses; and assigning, by the processing system, a first IPv4address in the first table to a first computing device in response todetermining the first IPv4 address in the first table is not currentlybeing used.
 19. The method of claim 18, comprising: training, by theprocessing system, the artificial intelligence techniques on thetraining data to recognize IPv6 addresses from a third data pool thatincludes the different types of data wherein the different types of datainclude a third group of IPv6 addresses; identifying, by the processingsystem, a third portion of the third group of IPv6 addresses from thesecond portion of the second group of non-IP address data from withinthe second data pool using the artificial intelligence techniques;selecting, by the processing system, the third portion of the thirdgroup of IPv6 addresses using the artificial intelligence techniques;and storing, by the processing system, the third portion of the thirdgroup of IPv6 addresses in a second table of IPv6 addresses.
 20. Themethod of claim 19, comprising assigning, by the processing system, afirst IPv6 address in the second table to a second computing device inresponse to determining the first IPv6 address in the second table isnot currently being used.